Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations121
Missing cells54
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.2 KiB
Average record size in memory196.0 B

Variable types

Categorical6
Numeric17
Boolean4

Alerts

calidad_de_agua is highly overall correlated with icaHigh correlation
campaña is highly overall correlated with cd_total_mg_l_menor_que and 7 other fieldsHigh correlation
cd_total_mg_l_menor_que is highly overall correlated with campaña and 4 other fieldsHigh correlation
colif_fecales_ufc_100ml is highly overall correlated with escher_coli_ufc_100ml and 1 other fieldsHigh correlation
color is highly overall correlated with ica and 2 other fieldsHigh correlation
cr_total_mg_l is highly overall correlated with campaña and 3 other fieldsHigh correlation
dbo_mg_l is highly overall correlated with tem_agua and 1 other fieldsHigh correlation
dqo_mg_l is highly overall correlated with campaña and 2 other fieldsHigh correlation
enteroc_ufc_100ml is highly overall correlated with escher_coli_ufc_100ml and 1 other fieldsHigh correlation
escher_coli_ufc_100ml is highly overall correlated with colif_fecales_ufc_100ml and 2 other fieldsHigh correlation
espumas is highly overall correlated with ica and 1 other fieldsHigh correlation
fosf_ortofos_mg_l is highly overall correlated with p_total_l_mg_lHigh correlation
hidr_deriv_petr_ug_l is highly overall correlated with campaña and 3 other fieldsHigh correlation
ica is highly overall correlated with calidad_de_agua and 8 other fieldsHigh correlation
microcistina_ug_l is highly overall correlated with campaña and 4 other fieldsHigh correlation
nh4_mg_l is highly overall correlated with icaHigh correlation
od is highly overall correlated with ica and 1 other fieldsHigh correlation
olores is highly overall correlated with color and 3 other fieldsHigh correlation
p_total_l_mg_l is highly overall correlated with fosf_ortofos_mg_lHigh correlation
ph is highly overall correlated with odHigh correlation
sitios is highly overall correlated with color and 1 other fieldsHigh correlation
tem_agua is highly overall correlated with campaña and 2 other fieldsHigh correlation
tem_aire is highly overall correlated with campaña and 2 other fieldsHigh correlation
turbiedad_ntu is highly overall correlated with campañaHigh correlation
espumas is highly imbalanced (64.9%) Imbalance
cr_total_mg_l is highly imbalanced (51.3%) Imbalance
colif_fecales_ufc_100ml has 23 (19.0%) missing values Missing
nitrato_mg_l has 2 (1.7%) missing values Missing
nh4_mg_l has 3 (2.5%) missing values Missing
p_total_l_mg_l has 3 (2.5%) missing values Missing
fosf_ortofos_mg_l has 8 (6.6%) missing values Missing
dbo_mg_l has 5 (4.1%) missing values Missing
dqo_mg_l has 2 (1.7%) missing values Missing
hidr_deriv_petr_ug_l has 4 (3.3%) missing values Missing
cr_total_mg_l has 3 (2.5%) missing values Missing
sitios is uniformly distributed Uniform
clorofila_a_ug_l has 10 (8.3%) zeros Zeros

Reproduction

Analysis started2024-11-06 03:36:27.869093
Analysis finished2024-11-06 03:37:42.807405
Duration1 minute and 14.94 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

sitios
Categorical

High correlation  Uniform 

Distinct36
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
Canal Villanueva y Río Luján
 
4
Canal Aliviador y Río Lujan
 
4
Río Carapachay y Arroyo Gallo Fiambre
 
4
Río Reconquista y Río Lujan
 
4
Toma de agua Club de Pesca
 
4
Other values (31)
101 

Length

Max length37
Median length28
Mean length22.735537
Min length8

Characters and Unicode

Total characters2751
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCanal Villanueva y Río Luján
2nd rowCanal Villanueva y Río Luján
3rd rowCanal Villanueva y Río Luján
4th rowCanal Villanueva y Río Luján
5th rowRío Lujan y Arroyo Caraguatá

Common Values

ValueCountFrequency (%)
Canal Villanueva y Río Luján 4
 
3.3%
Canal Aliviador y Río Lujan 4
 
3.3%
Río Carapachay y Arroyo Gallo Fiambre 4
 
3.3%
Río Reconquista y Río Lujan 4
 
3.3%
Toma de agua Club de Pesca 4
 
3.3%
Río Lujan y Canal San Fernando 4
 
3.3%
Reserva Ecológica 4
 
3.3%
Playa Espigón de Pacheco 4
 
3.3%
Costa y Melo 4
 
3.3%
Puerto de Olivos Espigón 4
 
3.3%
Other values (26) 81
66.9%

Length

2024-11-06T00:37:43.026523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
y 35
 
7.2%
río 33
 
6.8%
de 21
 
4.3%
arroyo 20
 
4.1%
lujan 15
 
3.1%
canal 12
 
2.5%
playa 11
 
2.3%
m 10
 
2.1%
400 10
 
2.1%
costa 9
 
1.9%
Other values (80) 308
63.6%

Most occurring characters

ValueCountFrequency (%)
382
 
13.9%
a 330
 
12.0%
o 223
 
8.1%
e 155
 
5.6%
r 149
 
5.4%
n 143
 
5.2%
l 140
 
5.1%
i 111
 
4.0%
y 74
 
2.7%
u 74
 
2.7%
Other values (45) 970
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1929
70.1%
Space Separator 382
 
13.9%
Uppercase Letter 373
 
13.6%
Decimal Number 59
 
2.1%
Open Punctuation 3
 
0.1%
Close Punctuation 3
 
0.1%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 330
17.1%
o 223
11.6%
e 155
 
8.0%
r 149
 
7.7%
n 143
 
7.4%
l 140
 
7.3%
i 111
 
5.8%
y 74
 
3.8%
u 74
 
3.8%
c 70
 
3.6%
Other values (17) 460
23.8%
Uppercase Letter
ValueCountFrequency (%)
P 55
14.7%
R 54
14.5%
C 54
14.5%
A 33
8.8%
E 32
8.6%
L 29
7.8%
S 25
6.7%
B 20
 
5.4%
D 15
 
4.0%
V 10
 
2.7%
Other values (8) 46
12.3%
Decimal Number
ValueCountFrequency (%)
0 26
44.1%
4 14
23.7%
6 9
 
15.3%
1 5
 
8.5%
3 3
 
5.1%
7 2
 
3.4%
Space Separator
ValueCountFrequency (%)
382
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2302
83.7%
Common 449
 
16.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 330
 
14.3%
o 223
 
9.7%
e 155
 
6.7%
r 149
 
6.5%
n 143
 
6.2%
l 140
 
6.1%
i 111
 
4.8%
y 74
 
3.2%
u 74
 
3.2%
c 70
 
3.0%
Other values (35) 833
36.2%
Common
ValueCountFrequency (%)
382
85.1%
0 26
 
5.8%
4 14
 
3.1%
6 9
 
2.0%
1 5
 
1.1%
( 3
 
0.7%
) 3
 
0.7%
3 3
 
0.7%
- 2
 
0.4%
7 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2674
97.2%
None 77
 
2.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
382
14.3%
a 330
 
12.3%
o 223
 
8.3%
e 155
 
5.8%
r 149
 
5.6%
n 143
 
5.3%
l 140
 
5.2%
i 111
 
4.2%
y 74
 
2.8%
u 74
 
2.8%
Other values (41) 893
33.4%
None
ValueCountFrequency (%)
í 42
54.5%
á 16
 
20.8%
ó 15
 
19.5%
ú 4
 
5.2%

campaña
Categorical

High correlation 

Distinct4
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
invierno
35 
primavera
33 
otoño
28 
verano
25 

Length

Max length9
Median length8
Mean length7.1652893
Min length5

Characters and Unicode

Total characters867
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowverano
2nd rowotoño
3rd rowinvierno
4th rowprimavera
5th rowotoño

Common Values

ValueCountFrequency (%)
invierno 35
28.9%
primavera 33
27.3%
otoño 28
23.1%
verano 25
20.7%

Length

2024-11-06T00:37:44.262007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T00:37:44.542512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
invierno 35
28.9%
primavera 33
27.3%
otoño 28
23.1%
verano 25
20.7%

Most occurring characters

ValueCountFrequency (%)
o 144
16.6%
r 126
14.5%
i 103
11.9%
n 95
11.0%
v 93
10.7%
e 93
10.7%
a 91
10.5%
p 33
 
3.8%
m 33
 
3.8%
t 28
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 867
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 144
16.6%
r 126
14.5%
i 103
11.9%
n 95
11.0%
v 93
10.7%
e 93
10.7%
a 91
10.5%
p 33
 
3.8%
m 33
 
3.8%
t 28
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 867
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 144
16.6%
r 126
14.5%
i 103
11.9%
n 95
11.0%
v 93
10.7%
e 93
10.7%
a 91
10.5%
p 33
 
3.8%
m 33
 
3.8%
t 28
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 839
96.8%
None 28
 
3.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 144
17.2%
r 126
15.0%
i 103
12.3%
n 95
11.3%
v 93
11.1%
e 93
11.1%
a 91
10.8%
p 33
 
3.9%
m 33
 
3.9%
t 28
 
3.3%
None
ValueCountFrequency (%)
ñ 28
100.0%

tem_agua
Real number (ℝ)

High correlation 

Distinct68
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.352893
Minimum7
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-11-06T00:37:44.820927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile10
Q114.4
median16
Q324
95-th percentile26
Maximum28
Range21
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation5.6036458
Coefficient of variation (CV)0.30532766
Kurtosis-1.3420684
Mean18.352893
Median Absolute Deviation (MAD)5
Skewness0.061825895
Sum2220.7
Variance31.400846
MonotonicityNot monotonic
2024-11-06T00:37:45.126965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.8 5
 
4.1%
24.7 5
 
4.1%
14.5 4
 
3.3%
14.4 4
 
3.3%
11 4
 
3.3%
10 4
 
3.3%
15.7 3
 
2.5%
13 3
 
2.5%
15 3
 
2.5%
26 3
 
2.5%
Other values (58) 83
68.6%
ValueCountFrequency (%)
7 2
1.7%
8 1
 
0.8%
10 4
3.3%
11 4
3.3%
11.4 1
 
0.8%
11.9 1
 
0.8%
12 2
1.7%
12.1 1
 
0.8%
12.8 1
 
0.8%
12.9 2
1.7%
ValueCountFrequency (%)
28 1
 
0.8%
27.8 1
 
0.8%
27.5 1
 
0.8%
27 1
 
0.8%
26.7 1
 
0.8%
26 3
2.5%
25.8 1
 
0.8%
25.7 1
 
0.8%
25.6 1
 
0.8%
25.5 2
1.7%

tem_aire
Real number (ℝ)

High correlation 

Distinct36
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.71157
Minimum4
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-11-06T00:37:45.474862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile7
Q112
median17
Q327
95-th percentile32
Maximum33
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.1672944
Coefficient of variation (CV)0.43648365
Kurtosis-1.321519
Mean18.71157
Median Absolute Deviation (MAD)7
Skewness0.11766636
Sum2264.1
Variance66.704698
MonotonicityNot monotonic
2024-11-06T00:37:45.775041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
28 11
 
9.1%
27 9
 
7.4%
11 9
 
7.4%
13 7
 
5.8%
26 7
 
5.8%
12 6
 
5.0%
32 5
 
4.1%
14.5 5
 
4.1%
8 5
 
4.1%
19 5
 
4.1%
Other values (26) 52
43.0%
ValueCountFrequency (%)
4 4
3.3%
7 3
 
2.5%
8 5
4.1%
9 2
 
1.7%
10 2
 
1.7%
11 9
7.4%
12 6
5.0%
12.5 1
 
0.8%
12.6 1
 
0.8%
12.8 4
3.3%
ValueCountFrequency (%)
33 1
 
0.8%
32.5 1
 
0.8%
32 5
4.1%
31 2
 
1.7%
29.1 3
 
2.5%
29 2
 
1.7%
28 11
9.1%
27 9
7.4%
26 7
5.8%
25 1
 
0.8%

od
Real number (ℝ)

High correlation 

Distinct112
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5659504
Minimum0.59
Maximum15.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-11-06T00:37:46.082602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.59
5-th percentile1.81
Q14.24
median5.95
Q38.84
95-th percentile12.39
Maximum15.2
Range14.61
Interquartile range (IQR)4.6

Descriptive statistics

Standard deviation3.1120806
Coefficient of variation (CV)0.47397261
Kurtosis-0.42087061
Mean6.5659504
Median Absolute Deviation (MAD)2.25
Skewness0.34841219
Sum794.48
Variance9.685046
MonotonicityNot monotonic
2024-11-06T00:37:46.407254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.91 2
 
1.7%
6.2 2
 
1.7%
4.82 2
 
1.7%
8.2 2
 
1.7%
5.95 2
 
1.7%
3.6 2
 
1.7%
4.07 2
 
1.7%
4.24 2
 
1.7%
7.58 2
 
1.7%
2.3 1
 
0.8%
Other values (102) 102
84.3%
ValueCountFrequency (%)
0.59 1
0.8%
0.74 1
0.8%
0.82 1
0.8%
1.21 1
0.8%
1.46 1
0.8%
1.61 1
0.8%
1.81 1
0.8%
2.16 1
0.8%
2.3 1
0.8%
2.35 1
0.8%
ValueCountFrequency (%)
15.2 1
0.8%
13.18 1
0.8%
12.72 1
0.8%
12.67 1
0.8%
12.61 1
0.8%
12.47 1
0.8%
12.39 1
0.8%
12.22 1
0.8%
12.04 1
0.8%
11.82 1
0.8%

ph
Real number (ℝ)

High correlation 

Distinct89
Distinct (%)73.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5647934
Minimum6.66
Maximum9.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-11-06T00:37:46.691738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6.66
5-th percentile6.75
Q16.96
median7.43
Q37.93
95-th percentile9.23
Maximum9.66
Range3
Interquartile range (IQR)0.97

Descriptive statistics

Standard deviation0.7412974
Coefficient of variation (CV)0.097993079
Kurtosis0.55831435
Mean7.5647934
Median Absolute Deviation (MAD)0.49
Skewness1.0747524
Sum915.34
Variance0.54952183
MonotonicityNot monotonic
2024-11-06T00:37:47.110776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8 4
 
3.3%
6.82 4
 
3.3%
6.96 3
 
2.5%
7.59 3
 
2.5%
7.04 2
 
1.7%
6.75 2
 
1.7%
7.05 2
 
1.7%
6.71 2
 
1.7%
6.79 2
 
1.7%
6.76 2
 
1.7%
Other values (79) 95
78.5%
ValueCountFrequency (%)
6.66 1
 
0.8%
6.7 1
 
0.8%
6.71 2
1.7%
6.74 1
 
0.8%
6.75 2
1.7%
6.76 2
1.7%
6.77 1
 
0.8%
6.79 2
1.7%
6.8 4
3.3%
6.81 1
 
0.8%
ValueCountFrequency (%)
9.66 1
0.8%
9.44 2
1.7%
9.36 1
0.8%
9.32 1
0.8%
9.25 1
0.8%
9.23 1
0.8%
9.22 1
0.8%
9.19 1
0.8%
9.18 1
0.8%
8.95 2
1.7%

olores
Boolean

High correlation 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
104 
True
17 
ValueCountFrequency (%)
False 104
86.0%
True 17
 
14.0%
2024-11-06T00:37:47.381997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

color
Boolean

High correlation 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
105 
True
16 
ValueCountFrequency (%)
False 105
86.8%
True 16
 
13.2%
2024-11-06T00:37:47.591939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

espumas
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
113 
True
 
8
ValueCountFrequency (%)
False 113
93.4%
True 8
 
6.6%
2024-11-06T00:37:47.833328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

mat_susp
Boolean

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
93 
True
28 
ValueCountFrequency (%)
False 93
76.9%
True 28
 
23.1%
2024-11-06T00:37:48.054819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

colif_fecales_ufc_100ml
Real number (ℝ)

High correlation  Missing 

Distinct39
Distinct (%)39.8%
Missing23
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean5323.6735
Minimum100
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-11-06T00:37:48.330360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile300
Q11375
median4000
Q38000
95-th percentile15000
Maximum20000
Range19900
Interquartile range (IQR)6625

Descriptive statistics

Standard deviation5011.8759
Coefficient of variation (CV)0.94143187
Kurtosis1.3071382
Mean5323.6735
Median Absolute Deviation (MAD)3000
Skewness1.3216096
Sum521720
Variance25118900
MonotonicityNot monotonic
2024-11-06T00:37:48.668622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
4000 10
 
8.3%
10000 9
 
7.4%
5000 8
 
6.6%
1000 7
 
5.8%
400 6
 
5.0%
3000 6
 
5.0%
2000 5
 
4.1%
20000 4
 
3.3%
15000 4
 
3.3%
300 4
 
3.3%
Other values (29) 35
28.9%
(Missing) 23
19.0%
ValueCountFrequency (%)
100 1
 
0.8%
110 1
 
0.8%
160 1
 
0.8%
300 4
3.3%
400 6
5.0%
500 1
 
0.8%
600 1
 
0.8%
700 1
 
0.8%
1000 7
5.8%
1200 1
 
0.8%
ValueCountFrequency (%)
20000 4
3.3%
15000 4
3.3%
14000 2
 
1.7%
12000 1
 
0.8%
10000 9
7.4%
9000 2
 
1.7%
8300 1
 
0.8%
8000 3
 
2.5%
7600 1
 
0.8%
7000 1
 
0.8%

escher_coli_ufc_100ml
Real number (ℝ)

High correlation 

Distinct50
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4341.1983
Minimum5
Maximum60000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-11-06T00:37:48.993067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile30
Q1200
median1000
Q33000
95-th percentile20000
Maximum60000
Range59995
Interquartile range (IQR)2800

Descriptive statistics

Standard deviation9663.8006
Coefficient of variation (CV)2.2260675
Kurtosis16.467971
Mean4341.1983
Median Absolute Deviation (MAD)900
Skewness3.9013565
Sum525285
Variance93389042
MonotonicityNot monotonic
2024-11-06T00:37:49.324610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 14
 
11.6%
1000 12
 
9.9%
2000 11
 
9.1%
200 5
 
4.1%
500 5
 
4.1%
400 5
 
4.1%
20 5
 
4.1%
3000 4
 
3.3%
1500 4
 
3.3%
10000 4
 
3.3%
Other values (40) 52
43.0%
ValueCountFrequency (%)
5 1
 
0.8%
20 5
 
4.1%
30 1
 
0.8%
50 2
 
1.7%
80 1
 
0.8%
100 14
11.6%
120 1
 
0.8%
150 1
 
0.8%
170 1
 
0.8%
200 5
 
4.1%
ValueCountFrequency (%)
60000 1
 
0.8%
50000 1
 
0.8%
48000 1
 
0.8%
37000 1
 
0.8%
30000 1
 
0.8%
25000 1
 
0.8%
20000 1
 
0.8%
18000 1
 
0.8%
12000 3
2.5%
10000 4
3.3%

enteroc_ufc_100ml
Real number (ℝ)

High correlation 

Distinct71
Distinct (%)58.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean872.23967
Minimum2
Maximum28000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-11-06T00:37:49.659906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q150
median200
Q3700
95-th percentile3700
Maximum28000
Range27998
Interquartile range (IQR)650

Descriptive statistics

Standard deviation2736.6504
Coefficient of variation (CV)3.1374983
Kurtosis81.832405
Mean872.23967
Median Absolute Deviation (MAD)173
Skewness8.429647
Sum105541
Variance7489255.6
MonotonicityNot monotonic
2024-11-06T00:37:49.931314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 12
 
9.9%
40 6
 
5.0%
300 6
 
5.0%
20 5
 
4.1%
800 5
 
4.1%
10 5
 
4.1%
30 4
 
3.3%
200 3
 
2.5%
240 3
 
2.5%
2000 2
 
1.7%
Other values (61) 70
57.9%
ValueCountFrequency (%)
2 1
 
0.8%
4 1
 
0.8%
5 2
 
1.7%
6 1
 
0.8%
10 5
4.1%
12 1
 
0.8%
15 1
 
0.8%
20 5
4.1%
27 1
 
0.8%
30 4
3.3%
ValueCountFrequency (%)
28000 1
0.8%
6200 1
0.8%
5600 1
0.8%
4800 1
0.8%
4600 2
1.7%
3700 1
0.8%
2800 1
0.8%
2300 1
0.8%
2100 2
1.7%
2000 2
1.7%

nitrato_mg_l
Real number (ℝ)

Missing 

Distinct50
Distinct (%)42.0%
Missing2
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean3.9067227
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-11-06T00:37:50.212571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12.2
median3.4
Q35.15
95-th percentile7.01
Maximum9
Range7
Interquartile range (IQR)2.95

Descriptive statistics

Standard deviation1.7660034
Coefficient of variation (CV)0.45204217
Kurtosis-0.30087701
Mean3.9067227
Median Absolute Deviation (MAD)1.3
Skewness0.78228479
Sum464.9
Variance3.118768
MonotonicityNot monotonic
2024-11-06T00:37:50.516209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 22
 
18.2%
2.9 5
 
4.1%
2.1 5
 
4.1%
3.5 5
 
4.1%
3.1 5
 
4.1%
2.2 4
 
3.3%
3 3
 
2.5%
3.2 3
 
2.5%
2.8 3
 
2.5%
5.1 3
 
2.5%
Other values (40) 61
50.4%
ValueCountFrequency (%)
2 22
18.2%
2.1 5
 
4.1%
2.2 4
 
3.3%
2.3 2
 
1.7%
2.5 1
 
0.8%
2.6 1
 
0.8%
2.7 1
 
0.8%
2.8 3
 
2.5%
2.9 5
 
4.1%
3 3
 
2.5%
ValueCountFrequency (%)
9 1
0.8%
8.2 1
0.8%
8.1 1
0.8%
7.9 1
0.8%
7.5 1
0.8%
7.1 1
0.8%
7 2
1.7%
6.9 1
0.8%
6.7 1
0.8%
6.6 2
1.7%

nh4_mg_l
Real number (ℝ)

High correlation  Missing 

Distinct70
Distinct (%)59.3%
Missing3
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean1.7387288
Minimum0.05
Maximum9.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-11-06T00:37:50.865837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.0985
Q10.4025
median0.975
Q32.675
95-th percentile5.43
Maximum9.7
Range9.65
Interquartile range (IQR)2.2725

Descriptive statistics

Standard deviation1.8908459
Coefficient of variation (CV)1.0874876
Kurtosis3.897974
Mean1.7387288
Median Absolute Deviation (MAD)0.795
Skewness1.8264364
Sum205.17
Variance3.5752984
MonotonicityNot monotonic
2024-11-06T00:37:51.165753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 6
 
5.0%
0.7 5
 
4.1%
1.1 4
 
3.3%
3.1 4
 
3.3%
0.3 4
 
3.3%
0.6 4
 
3.3%
0.98 3
 
2.5%
0.2 3
 
2.5%
0.9 3
 
2.5%
0.8 3
 
2.5%
Other values (60) 79
65.3%
ValueCountFrequency (%)
0.05 2
 
1.7%
0.06 2
 
1.7%
0.08 1
 
0.8%
0.09 1
 
0.8%
0.1 6
5.0%
0.11 1
 
0.8%
0.14 1
 
0.8%
0.15 1
 
0.8%
0.16 1
 
0.8%
0.2 3
2.5%
ValueCountFrequency (%)
9.7 1
0.8%
9.3 1
0.8%
7 1
0.8%
6 1
0.8%
5.7 1
0.8%
5.6 1
0.8%
5.4 1
0.8%
5.3 1
0.8%
5 1
0.8%
4.7 1
0.8%

p_total_l_mg_l
Real number (ℝ)

High correlation  Missing 

Distinct59
Distinct (%)50.0%
Missing3
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean0.51237288
Minimum0.19
Maximum1.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-11-06T00:37:51.426273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.19
5-th percentile0.2
Q10.28
median0.42
Q30.65
95-th percentile1.1
Maximum1.5
Range1.31
Interquartile range (IQR)0.37

Descriptive statistics

Standard deviation0.30096903
Coefficient of variation (CV)0.58740234
Kurtosis0.87001089
Mean0.51237288
Median Absolute Deviation (MAD)0.17
Skewness1.1683315
Sum60.46
Variance0.090582355
MonotonicityNot monotonic
2024-11-06T00:37:51.717036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 11
 
9.1%
0.36 6
 
5.0%
1.1 4
 
3.3%
0.31 4
 
3.3%
0.22 4
 
3.3%
0.25 3
 
2.5%
0.57 3
 
2.5%
0.27 3
 
2.5%
0.33 3
 
2.5%
0.75 3
 
2.5%
Other values (49) 74
61.2%
ValueCountFrequency (%)
0.19 2
 
1.7%
0.2 11
9.1%
0.21 2
 
1.7%
0.22 4
 
3.3%
0.23 1
 
0.8%
0.24 1
 
0.8%
0.25 3
 
2.5%
0.26 2
 
1.7%
0.27 3
 
2.5%
0.28 3
 
2.5%
ValueCountFrequency (%)
1.5 1
 
0.8%
1.4 1
 
0.8%
1.3 2
1.7%
1.2 1
 
0.8%
1.1 4
3.3%
1 2
1.7%
0.94 1
 
0.8%
0.92 1
 
0.8%
0.89 1
 
0.8%
0.88 1
 
0.8%

fosf_ortofos_mg_l
Real number (ℝ)

High correlation  Missing 

Distinct53
Distinct (%)46.9%
Missing8
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean0.36752212
Minimum0.1
Maximum0.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-11-06T00:37:51.998235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.11
Q10.25
median0.34
Q30.48
95-th percentile0.688
Maximum0.87
Range0.77
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation0.17096121
Coefficient of variation (CV)0.46517256
Kurtosis-0.14899123
Mean0.36752212
Median Absolute Deviation (MAD)0.11
Skewness0.53540427
Sum41.53
Variance0.029227734
MonotonicityNot monotonic
2024-11-06T00:37:52.308646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 7
 
5.8%
0.1 5
 
4.1%
0.29 5
 
4.1%
0.37 5
 
4.1%
0.2 5
 
4.1%
0.51 4
 
3.3%
0.36 4
 
3.3%
0.63 3
 
2.5%
0.44 3
 
2.5%
0.32 3
 
2.5%
Other values (43) 69
57.0%
(Missing) 8
 
6.6%
ValueCountFrequency (%)
0.1 5
4.1%
0.11 3
2.5%
0.12 1
 
0.8%
0.13 1
 
0.8%
0.14 2
 
1.7%
0.15 1
 
0.8%
0.16 1
 
0.8%
0.18 1
 
0.8%
0.19 1
 
0.8%
0.2 5
4.1%
ValueCountFrequency (%)
0.87 1
 
0.8%
0.76 1
 
0.8%
0.75 1
 
0.8%
0.73 1
 
0.8%
0.71 1
 
0.8%
0.7 1
 
0.8%
0.68 1
 
0.8%
0.63 3
2.5%
0.62 1
 
0.8%
0.61 1
 
0.8%

dbo_mg_l
Real number (ℝ)

High correlation  Missing 

Distinct55
Distinct (%)47.4%
Missing5
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean4.562069
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-11-06T00:37:52.634598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12.475
median4.15
Q36.3
95-th percentile8.725
Maximum10
Range8
Interquartile range (IQR)3.825

Descriptive statistics

Standard deviation2.2076805
Coefficient of variation (CV)0.48392089
Kurtosis-0.69923
Mean4.562069
Median Absolute Deviation (MAD)1.85
Skewness0.56407183
Sum529.2
Variance4.8738531
MonotonicityNot monotonic
2024-11-06T00:37:52.970214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 19
 
15.7%
2.1 4
 
3.3%
3 4
 
3.3%
6 3
 
2.5%
4.1 3
 
2.5%
6.8 3
 
2.5%
6.3 3
 
2.5%
5.8 3
 
2.5%
2.7 3
 
2.5%
4.8 3
 
2.5%
Other values (45) 68
56.2%
(Missing) 5
 
4.1%
ValueCountFrequency (%)
2 19
15.7%
2.1 4
 
3.3%
2.2 2
 
1.7%
2.3 2
 
1.7%
2.4 2
 
1.7%
2.5 1
 
0.8%
2.7 3
 
2.5%
2.8 1
 
0.8%
2.9 1
 
0.8%
3 4
 
3.3%
ValueCountFrequency (%)
10 1
0.8%
9.8 1
0.8%
9.4 1
0.8%
8.9 2
1.7%
8.8 1
0.8%
8.7 1
0.8%
8.6 1
0.8%
8.5 1
0.8%
8.1 1
0.8%
7.7 1
0.8%

dqo_mg_l
Real number (ℝ)

High correlation  Missing 

Distinct46
Distinct (%)38.7%
Missing2
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean40.709244
Minimum2.2
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-11-06T00:37:53.269712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile4.3
Q133.5
median50
Q350
95-th percentile64.1
Maximum88
Range85.8
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation19.218908
Coefficient of variation (CV)0.47210183
Kurtosis-0.056320831
Mean40.709244
Median Absolute Deviation (MAD)5
Skewness-0.71637281
Sum4844.4
Variance369.36644
MonotonicityNot monotonic
2024-11-06T00:37:53.566702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
50 56
46.3%
37 5
 
4.1%
31 3
 
2.5%
5.6 3
 
2.5%
43 2
 
1.7%
5.2 2
 
1.7%
35 2
 
1.7%
57 2
 
1.7%
34 2
 
1.7%
62 2
 
1.7%
Other values (36) 40
33.1%
ValueCountFrequency (%)
2.2 1
0.8%
2.8 1
0.8%
3.3 1
0.8%
3.9 1
0.8%
4.1 1
0.8%
4.3 2
1.7%
4.9 1
0.8%
5 1
0.8%
5.1 1
0.8%
5.2 2
1.7%
ValueCountFrequency (%)
88 1
0.8%
82 1
0.8%
73 2
1.7%
67 1
0.8%
65 1
0.8%
64 1
0.8%
62 2
1.7%
59 1
0.8%
58 1
0.8%
57 2
1.7%

turbiedad_ntu
Real number (ℝ)

High correlation 

Distinct49
Distinct (%)40.8%
Missing1
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean26.310833
Minimum4.9
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-11-06T00:37:53.900656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4.9
5-th percentile7.095
Q116
median27
Q330.25
95-th percentile50.25
Maximum80
Range75.1
Interquartile range (IQR)14.25

Descriptive statistics

Standard deviation14.750438
Coefficient of variation (CV)0.56062224
Kurtosis2.4369674
Mean26.310833
Median Absolute Deviation (MAD)8
Skewness1.2597514
Sum3157.3
Variance217.57543
MonotonicityNot monotonic
2024-11-06T00:37:54.200933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
30 25
20.7%
16 6
 
5.0%
21 5
 
4.1%
31 4
 
3.3%
50 4
 
3.3%
33 4
 
3.3%
29 4
 
3.3%
13 3
 
2.5%
45 3
 
2.5%
19 3
 
2.5%
Other values (39) 59
48.8%
ValueCountFrequency (%)
4.9 1
0.8%
5.2 1
0.8%
5.3 1
0.8%
5.9 1
0.8%
6 1
0.8%
7 1
0.8%
7.1 1
0.8%
7.6 1
0.8%
8.7 1
0.8%
9 1
0.8%
ValueCountFrequency (%)
80 1
 
0.8%
75 2
1.7%
70 1
 
0.8%
60 1
 
0.8%
55 1
 
0.8%
50 4
3.3%
45 3
2.5%
43 1
 
0.8%
40 2
1.7%
39 1
 
0.8%

hidr_deriv_petr_ug_l
Real number (ℝ)

High correlation  Missing 

Distinct17
Distinct (%)14.5%
Missing4
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean94.930769
Minimum6.9
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-11-06T00:37:54.500621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile50
Q1100
median100
Q3100
95-th percentile102
Maximum150
Range143.1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.757309
Coefficient of variation (CV)0.19758935
Kurtosis7.7854698
Mean94.930769
Median Absolute Deviation (MAD)0
Skewness-2.1916226
Sum11106.9
Variance351.83663
MonotonicityNot monotonic
2024-11-06T00:37:54.763920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
100 93
76.9%
110 3
 
2.5%
70 2
 
1.7%
45 2
 
1.7%
80 2
 
1.7%
50 2
 
1.7%
60 2
 
1.7%
75 2
 
1.7%
150 1
 
0.8%
120 1
 
0.8%
Other values (7) 7
 
5.8%
(Missing) 4
 
3.3%
ValueCountFrequency (%)
6.9 1
0.8%
16 1
0.8%
39 1
0.8%
45 2
1.7%
50 2
1.7%
60 2
1.7%
65 1
0.8%
70 2
1.7%
75 2
1.7%
80 2
1.7%
ValueCountFrequency (%)
150 1
 
0.8%
140 1
 
0.8%
120 1
 
0.8%
110 3
 
2.5%
100 93
76.9%
95 1
 
0.8%
85 1
 
0.8%
80 2
 
1.7%
75 2
 
1.7%
70 2
 
1.7%

cr_total_mg_l
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)3.4%
Missing3
Missing (%)2.5%
Memory size1.9 KiB
0.005
87 
1.0
28 
0.0052
 
2
0.0057
 
1

Length

Max length6
Median length5
Mean length4.5508475
Min length3

Characters and Unicode

Total characters537
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row0.005
2nd row1.0
3rd row0.005
4th row0.005
5th row1.0

Common Values

ValueCountFrequency (%)
0.005 87
71.9%
1.0 28
 
23.1%
0.0052 2
 
1.7%
0.0057 1
 
0.8%
(Missing) 3
 
2.5%

Length

2024-11-06T00:37:55.049692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T00:37:55.283128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.005 87
73.7%
1.0 28
 
23.7%
0.0052 2
 
1.7%
0.0057 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 298
55.5%
. 118
 
22.0%
5 90
 
16.8%
1 28
 
5.2%
2 2
 
0.4%
7 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 419
78.0%
Other Punctuation 118
 
22.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 298
71.1%
5 90
 
21.5%
1 28
 
6.7%
2 2
 
0.5%
7 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 118
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 298
55.5%
. 118
 
22.0%
5 90
 
16.8%
1 28
 
5.2%
2 2
 
0.4%
7 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 298
55.5%
. 118
 
22.0%
5 90
 
16.8%
1 28
 
5.2%
2 2
 
0.4%
7 1
 
0.2%

cd_total_mg_l_menor_que
Categorical

High correlation 

Distinct3
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
0.001
91 
0.005
26 
0.01
 
4

Length

Max length5
Median length5
Mean length4.9669421
Min length4

Characters and Unicode

Total characters601
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.001
2nd row0.005
3rd row0.001
4th row0.001
5th row0.005

Common Values

ValueCountFrequency (%)
0.001 91
75.2%
0.005 26
 
21.5%
0.01 4
 
3.3%

Length

2024-11-06T00:37:55.546904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T00:37:55.777014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.001 91
75.2%
0.005 26
 
21.5%
0.01 4
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 359
59.7%
. 121
 
20.1%
1 95
 
15.8%
5 26
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 480
79.9%
Other Punctuation 121
 
20.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 359
74.8%
1 95
 
19.8%
5 26
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 359
59.7%
. 121
 
20.1%
1 95
 
15.8%
5 26
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 359
59.7%
. 121
 
20.1%
1 95
 
15.8%
5 26
 
4.3%

clorofila_a_ug_l
Real number (ℝ)

Zeros 

Distinct33
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.015625289
Minimum0
Maximum0.221
Zeros10
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-11-06T00:37:56.003280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.01
Q30.01
95-th percentile0.061
Maximum0.221
Range0.221
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.026896722
Coefficient of variation (CV)1.7213583
Kurtosis31.752148
Mean0.015625289
Median Absolute Deviation (MAD)0
Skewness5.1087747
Sum1.89066
Variance0.00072343365
MonotonicityNot monotonic
2024-11-06T00:37:56.246915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.01 69
57.0%
0 10
 
8.3%
0.011 3
 
2.5%
0.001 3
 
2.5%
0.00415 2
 
1.7%
0.00119 2
 
1.7%
0.009 2
 
1.7%
0.00237 2
 
1.7%
0.013 2
 
1.7%
0.018 2
 
1.7%
Other values (23) 24
 
19.8%
ValueCountFrequency (%)
0 10
8.3%
0.001 3
 
2.5%
0.00119 2
 
1.7%
0.00178 1
 
0.8%
0.00237 2
 
1.7%
0.00356 1
 
0.8%
0.00415 2
 
1.7%
0.005 2
 
1.7%
0.006 1
 
0.8%
0.007 1
 
0.8%
ValueCountFrequency (%)
0.221 1
0.8%
0.131 1
0.8%
0.11036 1
0.8%
0.08366 1
0.8%
0.07239 1
0.8%
0.06289 1
0.8%
0.061 1
0.8%
0.04331 1
0.8%
0.04213 1
0.8%
0.04153 1
0.8%

microcistina_ug_l
Categorical

High correlation 

Distinct4
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
0.2
68 
1.0
28 
0.15
24 
0.8
 
1

Length

Max length4
Median length3
Mean length3.1983471
Min length3

Characters and Unicode

Total characters387
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row0.15
2nd row1.0
3rd row0.2
4th row0.2
5th row1.0

Common Values

ValueCountFrequency (%)
0.2 68
56.2%
1.0 28
23.1%
0.15 24
 
19.8%
0.8 1
 
0.8%

Length

2024-11-06T00:37:56.518258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T00:37:56.719531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.2 68
56.2%
1.0 28
23.1%
0.15 24
 
19.8%
0.8 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 121
31.3%
. 121
31.3%
2 68
17.6%
1 52
13.4%
5 24
 
6.2%
8 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 266
68.7%
Other Punctuation 121
31.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 121
45.5%
2 68
25.6%
1 52
19.5%
5 24
 
9.0%
8 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 121
31.3%
. 121
31.3%
2 68
17.6%
1 52
13.4%
5 24
 
6.2%
8 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 121
31.3%
. 121
31.3%
2 68
17.6%
1 52
13.4%
5 24
 
6.2%
8 1
 
0.3%

ica
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)33.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.297521
Minimum26
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-11-06T00:37:57.018049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile30
Q139
median44
Q350
95-th percentile64
Maximum74
Range48
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.8290764
Coefficient of variation (CV)0.21698928
Kurtosis0.17711953
Mean45.297521
Median Absolute Deviation (MAD)6
Skewness0.47343723
Sum5481
Variance96.610744
MonotonicityNot monotonic
2024-11-06T00:37:57.333892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
49 9
 
7.4%
42 8
 
6.6%
46 8
 
6.6%
43 7
 
5.8%
38 6
 
5.0%
41 6
 
5.0%
39 5
 
4.1%
48 5
 
4.1%
40 4
 
3.3%
58 4
 
3.3%
Other values (30) 59
48.8%
ValueCountFrequency (%)
26 3
2.5%
28 2
1.7%
29 1
 
0.8%
30 2
1.7%
31 1
 
0.8%
33 1
 
0.8%
34 4
3.3%
35 3
2.5%
36 3
2.5%
37 3
2.5%
ValueCountFrequency (%)
74 1
 
0.8%
72 1
 
0.8%
67 1
 
0.8%
65 2
1.7%
64 2
1.7%
62 1
 
0.8%
61 1
 
0.8%
60 3
2.5%
59 1
 
0.8%
58 4
3.3%

calidad_de_agua
Categorical

High correlation 

Distinct3
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
Extremadamente deteriorada
63 
Muy deteriorada
53 
Deteriorada
 
5

Length

Max length26
Median length26
Mean length20.561983
Min length11

Characters and Unicode

Total characters2488
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExtremadamente deteriorada
2nd rowMuy deteriorada
3rd rowMuy deteriorada
4th rowMuy deteriorada
5th rowExtremadamente deteriorada

Common Values

ValueCountFrequency (%)
Extremadamente deteriorada 63
52.1%
Muy deteriorada 53
43.8%
Deteriorada 5
 
4.1%

Length

2024-11-06T00:37:57.654975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T00:37:57.907926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
deteriorada 121
51.1%
extremadamente 63
26.6%
muy 53
22.4%

Most occurring characters

ValueCountFrequency (%)
e 431
17.3%
a 368
14.8%
r 305
12.3%
d 300
12.1%
t 247
9.9%
m 126
 
5.1%
i 121
 
4.9%
o 121
 
4.9%
116
 
4.7%
E 63
 
2.5%
Other values (6) 290
11.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2251
90.5%
Uppercase Letter 121
 
4.9%
Space Separator 116
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 431
19.1%
a 368
16.3%
r 305
13.5%
d 300
13.3%
t 247
11.0%
m 126
 
5.6%
i 121
 
5.4%
o 121
 
5.4%
x 63
 
2.8%
n 63
 
2.8%
Other values (2) 106
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
E 63
52.1%
M 53
43.8%
D 5
 
4.1%
Space Separator
ValueCountFrequency (%)
116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2372
95.3%
Common 116
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 431
18.2%
a 368
15.5%
r 305
12.9%
d 300
12.6%
t 247
10.4%
m 126
 
5.3%
i 121
 
5.1%
o 121
 
5.1%
E 63
 
2.7%
x 63
 
2.7%
Other values (5) 227
9.6%
Common
ValueCountFrequency (%)
116
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 431
17.3%
a 368
14.8%
r 305
12.3%
d 300
12.1%
t 247
9.9%
m 126
 
5.1%
i 121
 
4.9%
o 121
 
4.9%
116
 
4.7%
E 63
 
2.5%
Other values (6) 290
11.7%

Interactions

2024-11-06T00:37:37.325109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:30.765572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:34.224439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:39.870559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:43.491608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:47.870949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:52.557109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:57.363757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:01.725700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:05.930320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:09.772890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:13.661971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:18.442237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:22.787095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:27.020344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:30.763403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:34.072516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:37.577521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:30.930749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:34.423757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:40.019762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:43.635007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:48.132308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:52.786571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:57.550170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:01.959476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:06.107125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:09.994074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:13.857108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:18.772263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:22.966830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:27.207659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:30.952816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:34.228514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:37.890132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:31.125434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:36.659282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:40.172682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:43.831941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:48.424100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:53.041478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:57.805584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:02.173197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:06.325957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:10.229981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:14.073530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:19.076835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:23.162164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:27.491735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:31.134588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:34.421836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:38.240500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:31.321832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:36.892978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:40.337047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:44.206144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:48.685104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:53.326070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:58.076158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:02.473977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:06.577497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:10.463345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:14.334518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:19.295572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:23.424814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:27.758176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:31.320091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:34.616380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:38.469465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:31.479360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:37.104335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:40.542005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:44.519589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:48.959204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:53.601494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:58.293991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:02.737924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:06.805583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:10.687176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:14.591137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:19.598119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:23.726001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:27.978981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:31.514696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:34.795286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:37:38.719032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:31.659883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:37.328300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:40.796392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T00:36:44.762847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-06T00:37:37.126138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-06T00:37:58.169525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
calidad_de_aguacampañacd_total_mg_l_menor_queclorofila_a_ug_lcolif_fecales_ufc_100mlcolorcr_total_mg_ldbo_mg_ldqo_mg_lenteroc_ufc_100mlescher_coli_ufc_100mlespumasfosf_ortofos_mg_lhidr_deriv_petr_ug_licamat_suspmicrocistina_ug_lnh4_mg_lnitrato_mg_lodoloresp_total_l_mg_lphsitiostem_aguatem_aireturbiedad_ntu
calidad_de_agua1.0000.1690.1190.0000.3960.3000.0900.1030.0250.0580.1580.2210.1530.3090.9530.2260.1600.2930.3050.3020.2650.1660.1650.2840.0530.1470.000
campaña0.1691.0000.6680.1800.1820.0670.5780.2890.5850.0000.0620.0550.2720.5120.2210.3970.8110.0920.3080.2370.0000.3830.1160.0000.5750.6070.508
cd_total_mg_l_menor_que0.1190.6681.0000.0000.0000.0830.6820.2310.5730.2650.3280.0820.2510.7750.1510.1870.6660.2200.2630.0000.0000.1660.0810.0000.4070.3370.464
clorofila_a_ug_l0.0000.1800.0001.000-0.0840.0000.3610.4810.088-0.103-0.1180.0000.134-0.0160.0750.0000.0000.1010.0260.2370.000-0.0280.3430.000-0.394-0.279-0.152
colif_fecales_ufc_100ml0.3960.1820.000-0.0841.0000.2450.0000.204-0.1300.4990.7090.4420.281-0.080-0.7380.1700.0000.095-0.107-0.2030.2120.074-0.2320.0590.000-0.040-0.159
color0.3000.0670.0830.0000.2451.0000.0000.0000.0000.3410.4270.4280.0000.0000.7060.4440.0000.3030.1920.2690.6460.2200.0930.5560.3100.1240.000
cr_total_mg_l0.0900.5780.6820.3610.0000.0001.0000.2820.4920.0000.0000.0170.1790.5110.1130.1850.5650.0000.2010.0000.0000.0000.0000.0000.2620.3060.347
dbo_mg_l0.1030.2890.2310.4810.2040.0000.2821.000-0.2180.1690.2210.0000.373-0.064-0.2510.0360.1740.415-0.0800.1490.0000.1050.2490.000-0.510-0.522-0.143
dqo_mg_l0.0250.5850.5730.088-0.1300.0000.492-0.2181.000-0.127-0.2920.107-0.2700.4490.1370.1970.501-0.214-0.0590.0300.0000.0250.0310.0000.0640.113-0.231
enteroc_ufc_100ml0.0580.0000.265-0.1030.4990.3410.0000.169-0.1271.0000.6600.4800.2570.105-0.7110.1980.0000.333-0.086-0.3200.3250.271-0.2070.2200.0420.010-0.204
escher_coli_ufc_100ml0.1580.0620.328-0.1180.7090.4270.0000.221-0.2920.6601.0000.3610.447-0.082-0.7960.0770.0000.442-0.189-0.4980.4050.294-0.3560.0000.0760.022-0.174
espumas0.2210.0550.0820.0000.4420.4280.0170.0000.1070.4800.3611.0000.0000.0000.5840.1890.0600.4960.0000.1530.5090.2560.0000.1990.0000.4240.199
fosf_ortofos_mg_l0.1530.2720.2510.1340.2810.0000.1790.373-0.2700.2570.4470.0001.000-0.268-0.4750.0000.2070.447-0.195-0.4810.2280.742-0.2270.2510.053-0.125-0.112
hidr_deriv_petr_ug_l0.3090.5120.775-0.016-0.0800.0000.511-0.0640.4490.105-0.0820.000-0.2681.000-0.0080.0300.512-0.096-0.169-0.0170.000-0.121-0.2430.0000.1410.158-0.184
ica0.9530.2210.1510.075-0.7380.7060.113-0.2510.137-0.711-0.7960.584-0.475-0.0081.0000.2950.115-0.5210.2610.5600.721-0.4590.3310.203-0.075-0.0530.161
mat_susp0.2260.3970.1870.0000.1700.4440.1850.0360.1970.1980.0770.1890.0000.0300.2951.0000.3100.1680.0000.2360.1800.4890.0410.2160.4170.3450.165
microcistina_ug_l0.1600.8110.6660.0000.0000.0000.5650.1740.5010.0000.0000.0600.2070.5120.1150.3101.0000.0000.3460.0240.0000.3920.0000.0000.3880.4510.444
nh4_mg_l0.2930.0920.2200.1010.0950.3030.0000.415-0.2140.3330.4420.4960.447-0.096-0.5210.1680.0001.000-0.356-0.4980.4380.322-0.2190.185-0.077-0.169-0.273
nitrato_mg_l0.3050.3080.2630.026-0.1070.1920.201-0.080-0.059-0.086-0.1890.000-0.195-0.1690.2610.0000.346-0.3561.0000.3990.236-0.0630.4240.4000.119-0.0100.257
od0.3020.2370.0000.237-0.2030.2690.0000.1490.030-0.320-0.4980.153-0.481-0.0170.5600.2360.024-0.4980.3991.0000.392-0.4760.6610.265-0.475-0.3170.135
olores0.2650.0000.0000.0000.2120.6460.0000.0000.0000.3250.4050.5090.2280.0000.7210.1800.0000.4380.2360.3921.0000.1600.2280.6880.0000.0000.000
p_total_l_mg_l0.1660.3830.166-0.0280.0740.2200.0000.1050.0250.2710.2940.2560.742-0.121-0.4590.4890.3920.322-0.063-0.4760.1601.000-0.1160.2580.1640.030-0.021
ph0.1650.1160.0810.343-0.2320.0930.0000.2490.031-0.207-0.3560.000-0.227-0.2430.3310.0410.000-0.2190.4240.6610.228-0.1161.0000.294-0.457-0.2620.052
sitios0.2840.0000.0000.0000.0590.5560.0000.0000.0000.2200.0000.1990.2510.0000.2030.2160.0000.1850.4000.2650.6880.2580.2941.0000.2350.0000.164
tem_agua0.0530.5750.407-0.3940.0000.3100.262-0.5100.0640.0420.0760.0000.0530.141-0.0750.4170.388-0.0770.119-0.4750.0000.164-0.4570.2351.0000.8020.021
tem_aire0.1470.6070.337-0.279-0.0400.1240.306-0.5220.1130.0100.0220.424-0.1250.158-0.0530.3450.451-0.169-0.010-0.3170.0000.030-0.2620.0000.8021.000-0.052
turbiedad_ntu0.0000.5080.464-0.152-0.1590.0000.347-0.143-0.231-0.204-0.1740.199-0.112-0.1840.1610.1650.444-0.2730.2570.1350.000-0.0210.0520.1640.021-0.0521.000

Missing values

2024-11-06T00:37:41.217724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-06T00:37:42.047106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-06T00:37:42.526052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

sitioscampañatem_aguatem_aireodpholorescolorespumasmat_suspcolif_fecales_ufc_100mlescher_coli_ufc_100mlenteroc_ufc_100mlnitrato_mg_lnh4_mg_lp_total_l_mg_lfosf_ortofos_mg_ldbo_mg_ldqo_mg_lturbiedad_ntuhidr_deriv_petr_ug_lcr_total_mg_lcd_total_mg_l_menor_queclorofila_a_ug_lmicrocistina_ug_licacalidad_de_agua
0Canal Villanueva y Río Lujánverano25.627.03.916.96FalseFalseFalseTrue4000.010003306.50.400.920.372.050.060.0100.00.0050.0010.000000.1542Extremadamente deteriorada
1Canal Villanueva y Río Lujánotoño15.613.08.286.79FalseFalseFalseFalse4000.0200503.00.980.200.204.150.030.0110.01.0000.0050.010001.0048Muy deteriorada
2Canal Villanueva y Río Lujáninvierno14.813.09.907.09FalseFalseFalseFalse1000.0200682.11.100.200.102.050.027.0100.00.0050.0010.010000.2064Muy deteriorada
3Canal Villanueva y Río Lujánprimavera24.429.07.286.91FalseFalseFalseTrue400.0200652.70.440.270.122.031.045.0100.00.0050.0010.003560.2055Muy deteriorada
5Río Lujan y Arroyo Caraguatáotoño15.713.06.907.00FalseFalseFalseFalseNaN10008002.90.570.190.204.350.030.0150.01.0000.0050.010001.0039Extremadamente deteriorada
6Río Lujan y Arroyo Caraguatáinvierno14.513.02.546.80FalseFalseFalseFalse10000.020001502.04.700.480.576.850.022.0100.00.0050.0010.013000.2043Extremadamente deteriorada
7Río Lujan y Arroyo Caraguatáprimavera25.433.06.206.90FalseFalseFalseTrue2000.01000584.23.300.200.142.037.036.0100.00.0050.0010.004150.2048Muy deteriorada
8Canal Aliviador y Río Lujanverano24.624.01.217.12TrueTrueTrueTrue20000.01800046002.86.000.680.566.350.017.0100.00.0050.0010.013000.1526Extremadamente deteriorada
9Canal Aliviador y Río Lujanotoño15.713.04.827.04FalseFalseFalseFalseNaN6000056002.94.300.510.514.85.630.070.01.0000.0050.010001.0036Extremadamente deteriorada
10Canal Aliviador y Río Lujaninvierno14.714.02.306.80TrueFalseFalseFalseNaN100002402.03.000.570.585.050.021.0100.00.0050.0010.010000.2034Extremadamente deteriorada
sitioscampañatem_aguatem_aireodpholorescolorespumasmat_suspcolif_fecales_ufc_100mlescher_coli_ufc_100mlenteroc_ufc_100mlnitrato_mg_lnh4_mg_lp_total_l_mg_lfosf_ortofos_mg_ldbo_mg_ldqo_mg_lturbiedad_ntuhidr_deriv_petr_ug_lcr_total_mg_lcd_total_mg_l_menor_queclorofila_a_ug_lmicrocistina_ug_licacalidad_de_agua
153Playa La Bagliardiverano20.026.05.097.43FalseFalseFalseTrue1000.03603003.55.401.40NaN3.973.018.0100.00.0050.0010.005000.1542Extremadamente deteriorada
155Playa La Bagliardiinvierno10.012.06.848.14TrueTrueTrueTrueNaN3000062002.0NaN1.50NaN5.5NaN8.7100.00.0050.0010.010000.2028Extremadamente deteriorada
156Balneario Municipalverano21.027.05.897.79FalseFalseFalseFalse400.0201803.30.980.78NaN2.450.0NaN100.0NaN0.0050.011000.1549Muy deteriorada
158Balneario Municipalinvierno8.010.09.708.04FalseFalseFalseFalse15000.0150010004.20.200.560.447.050.029.0100.00.0050.0010.010000.2041Extremadamente deteriorada
159Playa La Bagliardiprimavera11.019.05.707.78FalseTrueFalseTrue10000.07003002.00.901.10NaN3.050.04.9100.00.0050.0010.010000.2039Extremadamente deteriorada
160Balneario Municipalprimavera11.019.07.588.21FalseFalseFalseFalse110.0290154.10.410.310.272.557.070.0100.00.0050.0010.041530.2062Muy deteriorada
161Playa La Balandraverano20.026.04.897.75FalseFalseFalseFalse1200.04002403.31.800.730.422.750.05.3100.00.0050.0010.006000.1546Muy deteriorada
163Playa La Balandrainvierno7.09.09.999.22FalseFalseFalseFalse300.020202.01.300.330.235.050.075.0100.00.0050.0010.010000.2046Muy deteriorada
164Playa La Balandrainvierno7.08.010.709.19FalseFalseFalseFalse4200.0802402.00.700.450.227.337.075.0100.00.0050.0010.010000.2043Extremadamente deteriorada
165Playa La Balandraprimavera11.019.08.298.39FalseTrueFalseTrue3600.017023.90.110.200.113.058.050.0100.00.0050.0010.083660.2049Muy deteriorada